Gregory A. Buck

The paper assesses how HPV burden, vaginal microbiota, and host factors predict CIN3 risk in a racially diverse cohort, finding that integrated models outperform HPV-only models.

TEMPTED improves analysis of longitudinal microbiome data by incorporating time as a continuous variable, achieving high accuracy in phenotype classification and identifying microbial markers linked to birth outcomes.

This study predicts preeclampsia using first trimester vaginal microbiome data with machine learning, achieving an auROC of 0.78 in the training set and 0.80 in an independent cohort.